A Reliable Energy Consumption Path - Finding Algorithm for Signalized Traffic Networks with Electric Vehicles Considering the Correlated Link Travel Speeds

Electric vehicles are an important part of urban green transportation network, and their popularization and application are of great significance to the sustainable development of cities. In recent years, electric vehicles continue to make up greater portions of the vehicle fleet, yet are restricted by battery capacity and shortages of charging stations. Reasonable path planning for electric vehicles is not only conducive to improving the utilization rate of road resources, easing traffic congestion, but also of great significance to the energy utilization rate of vehicles and reducing greenhouse gas emissions. This paper deals with a reliable energy consumption path-finding (RECPF) algorithm for signalized traffic networks with electric vehicles in the presence of uncertainty. The RECPF problem is formulated as the probability of completing a trip without exhausting a given battery energy budget. We extend RECPF models accounting for the link travel speed correlations and delays at signalized intersections. Addressing the nonadditivity and nonlinearity of the suggested optimization model, a modified heuristic algorithm based on the Dijkstra and k shortest path algorithms is proposed to tackle the RECPF problems. The grid-based road network of Hong Kong is employed to demonstrate efficacy and usefulness of the established algorithm. The obtained results reveal that incorporating link travel speed correlations and delays at signalized intersections bring about more accurate prediction of electric vehicle energy consumption, producing more efficient optimal reliable path recommendations